from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 predictions (58.33%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.001	NaN	5.957	0.0	-1	1	NaN	0.053	0.004	NaN	0.256	0.257	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.001	NaN	6.199	0.0	-1	5	NaN	0.051	0.001	NaN	0.254	0.254	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	NaN	5.674	0.0	1	100	NaN	0.051	0.001	NaN	0.278	0.278	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	NaN	6.445	0.0	-1	100	NaN	0.051	0.001	NaN	0.245	0.245	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	NaN	6.560	0.0	1	5	NaN	0.053	0.002	NaN	0.231	0.231	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.001	NaN	6.386	0.0	1	1	NaN	0.050	0.001	NaN	0.250	0.250	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.431	0.0	-1	1	NaN	0.007	0.000	NaN	0.535	0.535	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.001	NaN	0.362	0.0	-1	5	NaN	0.007	0.000	NaN	0.611	0.611	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.001	NaN	0.405	0.0	1	100	NaN	0.007	0.000	NaN	0.561	0.562	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.406	0.0	-1	100	NaN	0.007	0.000	NaN	0.548	0.548	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.400	0.0	1	5	NaN	0.007	0.000	NaN	0.559	0.560	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.420	0.0	1	1	NaN	0.008	0.001	NaN	0.507	0.509	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.124	0.217	NaN	0.000	0.002	-1	1	0.663	0.404	0.009	0.687	5.261	5.262	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.003	NaN	0.000	0.025	-1	1	1.000	0.011	0.001	1.000	2.326	2.332	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.700	0.059	NaN	0.000	0.003	-1	5	0.757	0.402	0.008	0.742	6.717	6.719	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.028	0.003	NaN	0.000	0.028	-1	5	1.000	0.011	0.001	1.000	2.592	2.601	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.111	0.038	NaN	0.000	0.002	1	100	0.882	0.449	0.007	0.875	4.702	4.702	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.001	NaN	0.000	0.023	1	100	1.000	0.011	0.001	0.000	2.148	2.150	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.666	0.107	NaN	0.000	0.003	-1	100	0.882	0.453	0.007	0.875	5.887	5.888	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.003	NaN	0.000	0.025	-1	100	1.000	0.011	0.000	0.000	2.260	2.262	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.067	0.015	NaN	0.000	0.002	1	5	0.757	0.403	0.008	0.742	5.124	5.125	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.000	NaN	0.000	0.023	1	5	1.000	0.011	0.000	1.000	2.172	2.173	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.385	0.012	NaN	0.001	0.001	1	1	0.663	0.400	0.010	0.687	3.462	3.463	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.001	NaN	0.000	0.022	1	1	1.000	0.010	0.000	1.000	2.138	2.140	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.602	0.031	NaN	0.000	0.002	-1	1	0.896	0.088	0.003	0.967	18.259	18.272	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.002	NaN	0.000	0.005	-1	1	1.000	0.001	0.000	1.000	8.283	8.342	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.305	0.029	NaN	0.000	0.002	-1	5	0.922	0.090	0.001	0.974	25.753	25.755	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.005	NaN	0.000	0.007	-1	5	1.000	0.001	0.000	1.000	11.955	12.092	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.832	0.016	NaN	0.000	0.002	1	100	0.929	0.140	0.003	0.975	13.127	13.129	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	100	1.000	0.001	0.000	1.000	4.287	4.300	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.329	0.040	NaN	0.000	0.002	-1	100	0.929	0.140	0.005	0.975	16.617	16.630	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.004	NaN	0.000	0.007	-1	100	1.000	0.001	0.000	1.000	10.580	10.636	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.843	0.022	NaN	0.000	0.002	1	5	0.922	0.089	0.001	0.974	20.767	20.769	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	5	1.000	0.001	0.000	1.000	5.141	5.177	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.165	0.014	NaN	0.000	0.001	1	1	0.896	0.089	0.003	0.967	13.114	13.123	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	NaN	0.000	0.002	1	1	1.000	0.001	0.000	1.000	3.586	3.606	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.291	0.038	NaN	0.035	0.0	-1	1	NaN	0.753	0.098	NaN	3.040	3.066	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.027	0.042	NaN	0.026	0.0	-1	5	NaN	0.694	0.007	NaN	4.364	4.365	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.222	0.189	NaN	0.025	0.0	1	100	NaN	0.697	0.028	NaN	4.623	4.626	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.173	0.090	NaN	0.025	0.0	-1	100	NaN	0.702	0.022	NaN	4.519	4.521	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.116	0.071	NaN	0.026	0.0	1	5	NaN	0.687	0.009	NaN	4.536	4.536	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.137	0.056	NaN	0.026	0.0	1	1	NaN	0.695	0.014	NaN	4.512	4.513	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	NaN	0.015	0.0	-1	1	NaN	0.004	0.002	NaN	0.277	0.320	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.024	0.0	-1	5	NaN	0.002	0.001	NaN	0.427	0.585	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.027	0.0	1	100	NaN	0.001	0.000	NaN	0.474	0.507	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.027	0.0	-1	100	NaN	0.001	0.000	NaN	0.613	0.616	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.026	0.0	1	5	NaN	0.001	0.000	NaN	0.587	0.591	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.026	0.0	1	1	NaN	0.001	0.000	NaN	0.685	0.688	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.841	1.153	NaN	0.000	0.001	-1	1	0.929	0.111	0.005	0.910	7.558	7.565	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	1.000	9.506	10.981	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.944	0.346	NaN	0.000	0.001	-1	5	0.946	0.198	0.006	0.941	4.769	4.771	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	NaN	0.000	0.003	-1	5	1.000	0.000	0.000	1.000	6.248	6.490	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	4.863	0.483	NaN	0.000	0.005	1	100	0.951	0.608	0.008	0.940	8.002	8.003	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	0.001	0.000	1.000	3.323	3.416	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2.913	0.312	NaN	0.000	0.003	-1	100	0.951	0.589	0.009	0.940	4.946	4.946	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	NaN	0.000	0.004	-1	100	1.000	0.001	0.000	1.000	5.097	5.220	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.538	0.458	NaN	0.000	0.002	1	5	0.946	0.199	0.005	0.941	7.724	7.726	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.001	NaN	0.000	0.001	1	5	1.000	0.000	0.000	1.000	3.357	3.562	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.855	0.388	NaN	0.000	0.001	1	1	0.929	0.110	0.004	0.910	7.756	7.760	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	1.000	3.071	3.116	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.029	0.016	NaN	0.001	0.000	-1	1	0.891	0.001	0.000	0.879	45.629	46.159	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.001	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	1.000	22.646	22.853	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.025	0.001	NaN	0.001	0.000	-1	5	0.911	0.001	0.000	0.905	27.278	27.306	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	5	1.000	0.000	0.000	1.000	17.007	17.115	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.038	0.012	NaN	0.000	0.000	1	100	0.894	0.005	0.000	0.917	7.201	7.210	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	100	1.000	0.000	0.000	1.000	7.367	7.432	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.042	0.007	NaN	0.000	0.000	-1	100	0.894	0.006	0.001	0.917	7.094	7.293	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	100	1.000	0.000	0.000	1.000	13.244	13.493	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.022	0.001	NaN	0.001	0.000	1	5	0.911	0.001	0.000	0.905	24.360	24.427	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	5	1.000	0.000	0.000	1.000	6.048	6.093	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.001	NaN	0.001	0.000	1	1	0.891	0.001	0.000	0.879	37.171	37.246	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	1.000	4.994	5.130	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.593	0.267	30	0.027	0.0	random	NaN	0.275	0.012	NaN	2.157	2.159	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.586	0.020	30	0.027	0.0	k-means++	NaN	0.308	0.005	NaN	1.903	1.904	See	See
6	KMeans_tall	fit	1000000	1000000	100	7.029	0.264	30	0.114	0.0	random	NaN	4.000	0.138	NaN	1.757	1.758	See	See
9	KMeans_tall	fit	1000000	1000000	100	7.337	0.096	30	0.109	0.0	k-means++	NaN	4.185	0.071	NaN	1.753	1.753	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.009	0.000	random	0.001	0.0	0.0	0.001	7.288	9.385	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	1.000	0.0	0.0	1.000	11.397	11.462	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.009	0.000	k-means++	0.001	0.0	0.0	0.001	9.383	9.628	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	1.000	0.0	0.0	1.000	11.076	11.115	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.414	0.000	random	0.002	0.0	0.0	0.002	7.020	7.256	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	random	1.000	0.0	0.0	1.000	9.801	9.866	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.001	30	0.393	0.000	k-means++	0.002	0.0	0.0	0.001	7.217	7.402	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	1.000	0.0	0.0	1.000	12.583	12.643	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	adjusted_rand_score_sklearn	adjusted_rand_score_sklearnex	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	0.000126	-0.000965	0.001090
4	KMeans_short	predict	10000	1000	2	0.001245	-0.000750	0.001995
7	KMeans_short	predict	10000	1000	100	0.278733	0.293767	0.015034
10	KMeans_short	predict	10000	1000	100	0.317011	0.256968	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.100	0.003	20	0.002	0.0	random	NaN	0.050	0.002	NaN	2.003	2.005	See	See
3	KMeans_short	fit	10000	10000	2	0.297	0.013	20	0.001	0.0	k-means++	NaN	0.131	0.003	NaN	2.267	2.267	See	See
6	KMeans_short	fit	10000	10000	100	0.319	0.009	20	0.025	0.0	random	NaN	0.254	0.006	NaN	1.258	1.258	See	See
9	KMeans_short	fit	10000	10000	100	1.088	0.011	20	0.007	0.0	k-means++	NaN	0.609	0.010	NaN	1.788	1.789	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	random	0.000	0.001	0.0	-0.001	3.472	3.485	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	random	1.000	0.000	0.0	1.000	11.291	11.342	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	k-means++	0.001	0.001	0.0	-0.001	3.251	3.271	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	k-means++	1.000	0.000	0.0	1.000	9.916	9.942	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.255	0.000	random	0.279	0.001	0.0	0.294	2.165	2.177	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	random	1.000	0.000	0.0	1.000	9.158	9.221	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.255	0.000	k-means++	0.317	0.001	0.0	0.257	2.151	2.157	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	k-means++	1.000	0.000	0.0	1.000	9.895	9.924	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	0.56	0.55	0.01
4	LogisticRegression	predict	1000	100	10000	0.35	0.28	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	15.559	0.489	[20]	0.051	0.000	NaN	NaN	NaN	NaN	NaN	2.956	0.044	NaN	5.265	5.265	See	See
3	LogisticRegression	fit	1000	1000	10000	1.446	0.683	[26]	0.055	0.001	NaN	NaN	NaN	NaN	NaN	1.139	0.032	NaN	1.269	1.270	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.001	[20]	1.618	0.0	NaN	NaN	NaN	NaN	0.56	0.001	0.001	0.55	0.570	1.064	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.011	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.000	1.00	0.334	0.336	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.000	[26]	3.358	0.0	NaN	NaN	NaN	NaN	0.35	0.006	0.001	0.28	0.377	0.379	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.000	[26]	0.703	0.0	NaN	NaN	NaN	NaN	0.00	0.002	0.000	0.00	0.066	0.066	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	r2_score_sklearn	r2_score_sklearnex	diff_r2_scores
1	Ridge	predict	1000	1000	10000	0.082567	0.122191	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.293	0.006	NaN	0.273	0.0	NaN	NaN	NaN	0.308	0.011	NaN	0.951	0.952	See	See
3	Ridge	fit	1000000	1000000	100	1.328	0.075	NaN	0.602	0.0	NaN	NaN	NaN	0.475	0.279	NaN	2.793	3.238	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.0	NaN	6.481	0.0	NaN	NaN	0.083	0.020	0.001	0.122	0.617	0.618	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	NaN	0.733	0.0	NaN	NaN	NaN	0.000	0.000	NaN	0.812	0.830	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	NaN	5.320	0.0	NaN	NaN	1.000	0.001	0.001	1.000	0.294	0.563	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	NaN	0.013	0.0	NaN	NaN	NaN	0.000	0.000	NaN	0.597	0.609	See	See